Widespread communications applications such as email, document sharing, and social networking are connecting more and more people. As people's contacts lists grow ever larger, it becomes more difficult to determine the most relevant people to receive a message or join in a conversation. For example, when a user considers calling a meeting of people to engage in a new project, the user may have to rely on his or her own memory to generate the appropriate personnel list. The user may thus leave out certain people who may be important, or inadvertently include people who may not be relevant. Other similar scenarios include identifying relevant recipients of an email, contacts on a social network, parties with whom to share documents, etc.
Existing automatic people recommendation techniques may recommend recipients using certain basic signals, such as first letters of a user-input name, or most frequently emailed contacts, etc. It would be desirable to leverage additional, deep contextual features of a communications item such as a message or conversation to improve the quality and relevance of people recommendations.